The SAWL system
SAWL supports the evaluation and comparison of semantic location algorithms (see below) on both simulated and real spaces. The main components are:- Space simulator: generation of the probability grid based on a model inspired by the Community-based Mobility Model (Musolesi&Muscolo 2007). The space is then populated by either imported or randomly generated sensitive places.
- Cloaking engine: provides an extensible set of semantic location cloaking method.
System architecture
Scenario: protecting sensitive semantic locations in LBS
- Users visit semantic locations (i.e.,geographical places) of different type (e.g., entertainment places)
- Semantic locations { f1,..fn} have different popularity: P(x in f1),...,P(x in fn)
- Semantic locations can be sensitive
- Privacy thread: associating users with sensitive semantic locations (e.g., hospitals) with a probability higher than a given threshold
Computational model and cloaking techniques
Goal: to bound the probability of user's association with sensitive locations while preserving QoSApproach:
- separating the generation of cloaked regions from the user's position enforcement in order to prevent reverse engineering attacks;
- personalizing privacy requirements through the privacy profile;
- utility measures;
- generating cloaked regions by expanding sensitive seeds